The Office for Research and Innovation at Stony Brook University invites you to attend the inaugural Wolf Den, an evening designed to bring together members of the regional innovation and entrepreneurial ecosystem.
Meet investors, researchers, startup founders, and business leaders to exchange ideas, foster collaboration, and strengthen connections that drive technology development and economic growth across Long Island.
Agenda
4:30 - 5:00 PM | Grab some cheer & mingle
5:00 - 5:40 PM | Welcome remarks and AI Panel
5:40 - 6:00PM | Featured lightning pitches
6:00 - 7:00 PM | Food, drinks and great conversations!
Attendees will have the opportunity to learn more about Stony Brook's entrepreneurship ecosystem, hear company pitches from emerging startups, and engage in meaningful networking with innovators, investors and community partners.
Refreshments will be served. Registration is required.
In partnership with Accelerate Long Island.
https://www.stonybrook.edu/commcms/innovation/_events/wolfden.php
AI on Campus: Your Thoughts, Your Future
Join the Conversation: Share Your Thoughts about Learning, Academics, and AI
The world of college is changing fast, and Artificial Intelligence (AI) is at the center of it. We are part of the Institute on AI, Pedagogy, and the Curriculum with AAC&U, and we need to hear from the people AI affects most: you!
This is an open discussion for all students to share their honest experiences, their top concerns, and their best ideas about AI in our academic environment. We'll be diving into these key questions:
How can AI actually make learning better or easier? What opportunities do you see for using AI tools to enhance your assignments, research, or skills?
What are your biggest worries about AI? Is it about cheating, being graded fairly, or preparing for the job market? How is AI impacting your workload or stress levels?
What specific tools, workshops, or policies would help you use AI responsibly and successfully? (Think training, software, or clear rules.)
Dates/Times:
Wednesday, 2/4 at 2pm
Thursday, 2/5 at 12pm
Please register in advance for the Zoom link.
Can't Make It? Share Your Feedback!
Don't worry if you can't attend! You can still share your thoughts via video in our AI Zoom Room or via email: rose.tirotta-esposito@stonybrook.edu.
Videos will not be shared publicly and comments will only be shared in aggregate.
Your voice matters. Come tell us how AI is affecting your studies, your stress, and your success!
Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)
Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)
Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)
Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)
Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)
Visual Analytics and Machine Learning for Biomedical Imaging Diagnosis
Arie Kaufman
We present an integrated approach using visual analytics and machine learning (ML) to diagnose abnormalities in 3D radiological imaging and biological microscopes. The primary example will involve 3D virtual pancreatography (VP), a novel visualization-ML procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes an ML-based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, an ML-based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists. Other applications include virtual colonoscopy, COVID-19, pathology, brain neurites, etc.
Biography: Arie Kaufman is Distinguished Professor and formerChair of the Department of Computer Science at Stony Brook University, where he is also Director of the Center for Visual Computing (CVC), and Chief Scientist at the Center of Excellence in Wireless and Information Technology (CEWIT).
He received his PhD in Computer Science at Ben-Gurion University of the Negev in 1977. He is known for his work in visualization, graphics, virtual reality, user interfaces, multimedia, and their applications, especially in bio-medicine. He is especially well known for his work on the 3-dimensional virtual colonoscopy, a revolutionary low-risk technique for colon cancer screening, and for pioneering the use of Graphics Processing Units (GPUs) and GPU-clusters. In 2012, he presided over the development and opening of the Reality Deck, the largest virtual reality display in the world, at Stony Brook University.
Kaufman was the founding Editor in Chief of IEEE Transactions on Visualization and Computer Graphics (TVCG), co-founded the IEEE Visualization Conference and Volume Graphics series, and is currently the director of IEEE Computer Society Technical Committee on Visualization and Graphics. He is an IEEE Fellow, ACM Fellow, winner of many awards, including the IEEE Visualization Career Award, and member of the European Academy of Sciences.
Steven Skiena is inviting you to a scheduled Zoom meeting.
Topic: AI Seminar: Arie Kaufman
Time: Apr 21, 2021 10:00 AM Eastern Time (US and Canada)
Join Zoom Meeting
https://stonybrook.zoom.us/j/
The first part of this work improves GTE performance by introducing Patch-level Distribution Prediction (PDP). Unlike traditional models that rely on strict pixel-wise regression, PDP models gaze as a distribution over patches, which better accounts for annotation variance and bridges the gap between target location and in/out-of-frame prediction. To address the laborious nature of data labeling, the second part presents GCDR, the first semi-supervised method for gaze following. By prompting large Visual Question Answering (VQA) models to generate initial Grad-CAM heatmaps and refining them with a diffusion model, this method achieves high performance with significantly fewer human annotations. The third part expands the applicability of GTE to multi-camera environments. By introducing the Multi-View Gaze Target (MVGT) dataset, along with two novel frameworks for integrating information between multiple views and predicting the gaze target across views, we explore a new direction that overcomes single-view limitations such as face occlusion and out-of-view targets.
Building on these foundations, the final part of this thesis proposes a new direction toward semantic social gaze understanding using next-generation multimodal Large Language Models (LLMs). Rather than focusing solely on geometric gaze target localization, we aim to enrich gaze prediction with semantic and relational interpretation in complex social scenes. To this end, we will leverage existing gaze following datasets to derive social gaze supervision, including mutual gaze and shared attention, and obtain aligned language descriptions of scene-level gaze behaviors. This proposed work will enable the model to not only locate gaze targets but also predict structured social gaze relations among individuals, meanwhile generating a concise natural-language summary describing the dominant gaze interactions. By integrating spatial gaze estimation, social relation reasoning, and language-based scene understanding within a unified multimodal model, this work takes an important step toward a holistic understanding of human gaze behavior in real-world environments.
Speaker: Qiaomu Miao
Date of Event
Joel H. Saltz, MD, PhD
SUNY Distinguished Professor Cherith Professor and Founding Chair
Department of Biomedical Informatics
Stony Brook University
Apostolos K. Tassiopoulos, MD, FACS
Professor of surgery and vice chair for quality and outcomes Chief of the Division of Vascular and Endovascular Surgery
Director of the Stony Brook Vascular Center Stony Brook Medicine
Title: Clinical applications of artificial intelligence to improve diagnosis and risk stratification for patients with aortic aneurysms
Time: Wednesday, Feb 17, 2021 3 pm - 4 pm
Join Zoom Meeting
https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISj...
Meeting ID: 956 1719 7636 Passcode: 924293
Topic: Responsible Artificial Intelligence: Promoting Health Equity for All
Speaker: Michael P. Cary, Jr., PhD, RN, FAAN.
Dr. Cary is a tenured Associate Professor at the Duke University School of Nursing. Dually trained as a health services researcher and applied health data scientist, Dr. Cary utilizes AI to investigate health disparities in aging populations, thereby promoting health equity and improving healthcare delivery. He co-directs HUMAINE™, an initiative dedicated to equipping nurses and healthcare professionals with the knowledge and skills necessary for the responsible use of AI in clinical practice.
Register: https://web.cvent.com/event/057978a5-a770-4de5-aca5-ad00287e4902/summary
Jerome Liang, PhD
Professor of Radiology, Biomedical Engineering, Electric and Computer Engineering, and Computer Science
Co-Director of Research
Department of Radiology
Artificial intelligence, machine learning and computer-aided diagnosis in cancer Imaging
February 11, 2021
12:00pm - 1:00pm
Virtual Seminar - Zoom
https://stonybrook.zoom.us/j/
Meeting ID: 981 5562 9970
Passcode: 950410
Host:
Wei Zhao, PhD
Professor of Radiology and Biomedical Engineering
Educational Objectives
Upon completion, participants should be able to:
(1) Learn different medical image representations of cancer attributes, such as heterogeneity, high tendency to grow, etc.
(2) Learn how computer (machine) can be trained (or programmed) to recognize the image representations.
(3) Learn how artificial intelligence can drive the machine learning to maximize the performance of computer-aided diagnosis (CADx).
Disclosure Statement
In compliance with the ACCME Standards for Commercial Support, everyone who is in a position to control the content of an educational activity provided by the School of Medicine is expected to disclose to the audience any relevant financial relationships with any commercial interest that relates to the content of his/her presentation.
The speaker, Jerome Liang, PhD, the planners; and the CME provider have no relevant financial relationship with a commercial interest (defined as any entity producing, marketing, re-selling, or distributing health care goods or services consumed by, or used on, patients), that relates to the content that will be discussed in the educational activity.
CONTINUING MEDICAL EDUCATION CREDITS
The School of Medicine, State University of New York at Stony Brook, is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
The School of Medicine, State University of New York at Stony Brook designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credits™. Physicians should only claim credit commensurate with the extent of their participation in the activity.
Should you be logging in Zoom by using your tablet or mobile device, please be sure to add your Full Name and/or Email for CME credit.
Join us at the Center for Excellence in Learning and Teaching (CELT) for an engaging workshop on Generative AI. This Zoom workshop is designed for faculty and staff members seeking to enhance their teaching methods and assessment strategies, foster student engagement, and navigate the evolving landscape of AI tools. Recording and slides will be sent to you.
Register here: https://stonybrook.zoom.us/meeting/register/tJ0qceisrjsrE9w1QtMkvSVw4lmr4h4x_Vqu